The Brutal Facts

While targeting using conventional response modelling is
generally much more effective than
either a "gut-feel" approach or blanket contact,
there are some unpalatable and under-appreciated facts.

It is normally assumed that the worst outcome
direct marketing activity can have
is to waste money.
In fact, some direct marketing provably drives away
business within certain segments,
and it is not unknown for it to drive away more business
in total than it generates.
This is especially true in retention
activity.

The use of control groups is a cornerstone of
state-of-the-art customer targeting,
and is certainly a prerequisite for allowing companies
to measure the true incremental
impact of any one-to-one customer management approach.
However, measuring the net effect
of a marketing programme is not the same as
optimizing that net effect.

Even in the most analytically sophisticated companies,
it is surprisingly common
for false conclusions to be drawn from control groups.
There are many and varied causes of this.
One common cause is that somewhere between
conception and execution of the campaign,
some influence causes control groups to be invalidated.
Another is that post-campaign analysis fails,
in one way or another, to perform a valid like-for-like
comparison, again leading to invalid conclusions.

Stochastic Solutions staff have deep experience of both
the design of direct marketing programmes and their post-campaign
analysis.
We can use this expertise to audit and verify the effectiveness
of current practices, and to work with companies to help ensure
the best planning of future activity.

In addition to this, we have deep expertise in a
scientific approach to taking marketing to the next stage,
using uplift modelling
to optimize the targeting of
direct marketing and customer management activity
to maximize the net (or incremental) impact of campaigns.

Of course, uplift modelling is no panacea,
and will not always lead to better results.
In some situations, the uplift approach adds nothing because
an uplift model ends up targeting the same
people as a conventional approach.
This situation pertains when incremental impact and
purchase rates are strongly correlated.
In other cases, typically when control groups are very small,
there is too much noise in the data for an uplift approach
to be effective at all, though remarkable strides have been
made in extracting meaningful patterns even with unreasonably
small control groups.

Frequently, however, the difference and uplift approach
makes is breath-taking.
We have used the uplift approach to
double the profitability
of already highly profitable campaigns;
in other cases, we have taken campaigns that were heavily
loss-making,
sometimes because of the sort of negative impacts discussed above,
and found segments of customers who can be profitably targeted.

Whatever stage of sophistication your business is at
with targeting or other customer decisioning,
Stochastic Solutions can help you to
take it to the next level.
If there is potential to benefit from more sophisticated
use of control groups and incremental modelling,
we can help you chart a path to gaining it.
If there's not, we can at least ensure that you have
in place the tools and methods to allow you to detect
that potential if and when it arises.